Agentic AI: Shaping the next era of smart technologies

What is agentic AI, and what does it mean?

At its core, agentic AI doesn’t just optimise isolated tasks - it reorganises entire value chains. The current scale of commitments in terms of investment have been touted by industry experts as a “once-in-a-lifetime reinvention” of technological paradigms. Major tech players to the likes of Microsoft, AWS, Alphabet, NVIDIA and Meta are actively investing in disruptive capital expenditure programs on - prioritising data center expansion, developing advanced graphic processing unit (GPUs), pioneering agentic AI and driving demand for robotics, custom chip development and AI model training infrastructure. 

Organisations unlock the full potential of agentic AI by grounding it in strong data management practices that ensure quality, accessibility and trust. Without reliable, well-managed data, agentic AI cannot make autonomous decisions or drive intelligent automation effectively.

Improving data quality with agentic AI

Agentic data management (ADM) transforms data observability by autonomously monitoring, healing pipelines and delivering real-time insights. AI agents handle tasks, adapt to change, and optimise the data ecosystem - crucial for GenAI use cases like unstructured, synthetic and vector data.

AI streamlines data cataloguing by automatically discovering, classifying, and tagging data assets to build a comprehensive, up-to-date catalogue. It strengthens data governance by enforcing policies, ensuring compliance and automating access controls. At the same time, it improves data quality by detecting and correcting errors, inconsistencies, and biases through intelligent data generation, augmentation and cleaning.

During data migration, autonomous AI systems plan and adjust workflows in response to real-time performance and feedback. In reconciliation processes, it continuously corrects mismatches and learns from past errors to enhance future accuracy. For data infrastructure, agentic AI takes full control - managing systems independently and aligning infrastructure decisions with strategic business goals.

Industry use cases of agentic AI

As agentic AI takes charge of core data functions, it also begins to reshape applications with unprecedented autonomy and intelligence:

Finance: Detecting fraudulent transactions in real time, initiating risk mitigation protocols, enhance credit risk assessments using predictive modelling.

Retail: Agentic AI has shown to deliver quick wins by offering autonomous customer support, adjusting prices based on factors like demand, inventory, competitor activity and customer behavior.

Healthcare: Autonomously monitoring patients, supporting clinical decisions and managing administrative workflows. It adapts in real time to personalise treatment while enhancing patient engagement - ultimately improving outcomes while reducing human workload.

Customer experience: Transformative in delivering hyper-personalised, context-aware support. It anticipates customer needs, engages in natural conversations, resolves issues proactively using sentiment analysis and assists human agents by retrieving relevant data.

Organisations empower data, reimagine technology, strengthen governance and enable people to lay the foundation for agentic AI, as outlined below: 

• Entities are ensuring agents have reliable access to enterprise data. They are also extending governance to unstructured data, closing quality gaps and enabling agents to operate with greater accuracy, adaptability and trust across diverse data environments.
• Organisations are redesigning their technology architecture to enable interoperability and scale for agentic AI. They are shifting from LLM-centric setups to agentic AI meshes that allow agents - custom-built or off-the-shelf, to operate seamlessly across fragmented systems, data and workflows. 
• Enterprises are proactively establishing governance frameworks to control agent autonomy and prevent sprawl. They define clear decision boundaries, monitor agent behavior, and implement audit mechanisms to ensure accountability. 
• Organisations are encouraged to foster a “human + agent” mindset cultural transformation through targeted training and by empowering early adopters. The need for creating roles like - prompt engineers to refine AI interactions, agent orchestrators to manage autonomous workflows, and human-in-the-loop designers to handle exceptions will create a future ready workforce.

Agentic AI is not just enhancing smart technologies - it’s redefining them. As enterprises retool their people, governance, architecture and data strategies, they’re laying the groundwork for a future where autonomous agents drive innovation, efficiency, and personalisation at scale. The agentic era is here - and the pioneers who embrace it now won’t just adapt to the future, they will help define it.

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